$\texttt{AMEND++}$: Benchmarking Eligibility Criteria Amendments in Clinical Trials

ACL ARR 2026 January Submission1195 Authors

Keywords: clinical trials, eligibility criteria, pre-training, amendment prediction

Abstract: Clinical trial amendments frequently introduce delays, increased costs, and administrative burden, with eligibility criteria being the most commonly amended component. We introduce eligibility criteria amendment prediction, a novel NLP task that aims to forecast whether the eligibility criteria of an initial trial protocol will undergo future amendments. To support this task, we release $\texttt{AMEND++}$, a benchmark suite comprising two datasets: $\texttt{AMEND}$, which captures eligibility-criteria version histories and amendment labels from public clinical trials, and $\verb|AMEND_LLM|$, a refined subset curated using an LLM-based denoising pipeline to isolate substantive changes. We further propose Change-Aware Masked Language Modeling (CAMLM), a revision-aware pretraining strategy that leverages historical edits to learn amendment-sensitive representations. Experiments across diverse baselines show that CAMLM consistently improves amendment prediction, enabling more robust and cost-effective clinical trial design.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: healthcare applications, clinical NLP

Contribution Types: NLP engineering experiment, Data resources

Languages Studied: English

Submission Number: 1195